263 results on '"Gooya, Ali"'
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102. 3D Medical Image Synthesis by Factorised Representation and Deformable Model Learning
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Joyce, Thomas, Kozerke, Sebastian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Burgos, Ninon, editor, Gooya, Ali, editor, and Svoboda, David, editor
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- 2019
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103. Physics-Informed Brain MRI Segmentation
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Borges, Pedro, Sudre, Carole, Varsavsky, Thomas, Thomas, David, Drobnjak, Ivana, Ourselin, Sebastien, Cardoso, M. Jorge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Burgos, Ninon, editor, Gooya, Ali, editor, and Svoboda, David, editor
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- 2019
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104. A Machine Learning Approach to Diffusion MRI Partial Volume Estimation
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Vallee, Emmanuel, Wu, Wenchuan, Galassi, Francesca, Jbabdi, Saad, Smith, Stephen, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
- Published
- 2018
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105. MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net for Multi-modal Alzheimer’s Classification
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Sikka, Apoorva, Peri, Skand Vishwanath, Bathula, Deepti R., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
- Published
- 2018
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106. Model-Based Generation of Synthetic 3D Time-Lapse Sequences of Multiple Mutually Interacting Motile Cells with Filopodia
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Peterlík, Igor, Svoboda, David, Ulman, Vladimír, Sorokin, Dmitry V., Maška, Martin, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
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- 2018
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107. Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks
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Shin, Hoo-Chang, Tenenholtz, Neil A., Rogers, Jameson K., Schwarz, Christopher G., Senjem, Matthew L., Gunter, Jeffrey L., Andriole, Katherine P., Michalski, Mark, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
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- 2018
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108. Cross-Modality Image Synthesis from Unpaired Data Using CycleGAN : Effects of Gradient Consistency Loss and Training Data Size
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Hiasa, Yuta, Otake, Yoshito, Takao, Masaki, Matsuoka, Takumi, Takashima, Kazuma, Carass, Aaron, Prince, Jerry L., Sugano, Nobuhiko, Sato, Yoshinobu, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
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- 2018
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109. Deep Boosted Regression for MR to CT Synthesis
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Kläser, Kerstin, Markiewicz, Pawel, Ranzini, Marta, Li, Wenqi, Modat, Marc, Hutton, Brian F., Atkinson, David, Thielemans, Kris, Cardoso, M. Jorge, Ourselin, Sébastien, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
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- 2018
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110. Unsupervised Learning for Cross-Domain Medical Image Synthesis Using Deformation Invariant Cycle Consistency Networks
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Wang, Chengjia, Macnaught, Gillian, Papanastasiou, Giorgos, MacGillivray, Tom, Newby, David, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
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- 2018
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111. Data Augmentation Using Synthetic Lesions Improves Machine Learning Detection of Microbleeds from MRI
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Momeni, Saba, Fazllolahi, Amir, Bourgeat, Pierrick, Raniga, Parnesh, Yates, Paul, Yassi, Nawaf, Desmond, Patricia, Fripp, Jurgen, Gao, Yongsheng, Salvado, Olivier, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
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- 2018
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112. Deep Harmonization of Inconsistent MR Data for Consistent Volume Segmentation
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Dewey, Blake E., Zhao, Can, Carass, Aaron, Oh, Jiwon, Calabresi, Peter A., van Zijl, Peter C. M., Prince, Jerry L., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
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- 2018
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113. Deep Learning Based Coronary Artery Motion Artifact Compensation Using Style-Transfer Synthesis in CT Images
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Jung, Sunghee, Lee, Soochahn, Jeon, Byunghwan, Jang, Yeonggul, Chang, Hyuk-Jae, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
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- 2018
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114. Tubular Network Formation Process Using 3D Cellular Potts Model
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Svoboda, David, Nečasová, Tereza, Tesařová, Lenka, Šimara, Pavel, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
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- 2018
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115. RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours
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Mehta, Raghav, Arbel, Tal, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
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- 2018
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116. Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-linear and Deep Learning Models
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Olliverre, Nathan, Yang, Guang, Slabaugh, Gregory, Reyes-Aldasoro, Constantino Carlos, Alonso, Eduardo, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
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- 2018
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117. Lung Nodule Synthesis Using CNN-Based Latent Data Representation
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Oliveira, Dario Augusto Borges, Viana, Matheus Palhares, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Gooya, Ali, editor, Goksel, Orcun, editor, Oguz, Ipek, editor, and Burgos, Ninon, editor
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- 2018
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118. Synthesizing CT from Ultrashort Echo-Time MR Images via Convolutional Neural Networks
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Roy, Snehashis, Butman, John A., Pham, Dzung L., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2017
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119. High Order Slice Interpolation for Medical Images
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Horváth, Antal, Pezold, Simon, Weigel, Matthias, Parmar, Katrin, Cattin, Philippe, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2017
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120. A Monte Carlo Framework for Low Dose CT Reconstruction Testing
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Mason, Jonathan H., Nailon, Willam H., Davies, Mike E., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2017
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121. Medical Image Processing and Numerical Simulation for Digital Hepatic Parenchymal Blood Flow
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Lebre, Marie-Ange, Arrouk, Khaled, Võ Văn, Anh-Khoa, Leborgne, Aurélie, Grand-Brochier, Manuel, Beaurepaire, Pierre, Vacavant, Antoine, Magnin, Benoît, Abergel, Armand, Chabrot, Pascal, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2017
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122. A Supervoxel Based Random Forest Synthesis Framework for Bidirectional MR/CT Synthesis
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Zhao, Can, Carass, Aaron, Lee, Junghoon, Jog, Amod, Prince, Jerry L., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2017
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123. Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results
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Ben-Cohen, Avi, Klang, Eyal, Raskin, Stephen P., Amitai, Michal Marianne, Greenspan, Hayit, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2017
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124. Region-Enhanced Joint Dictionary Learning for Cross-Modality Synthesis in Diffusion Tensor Imaging
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Wang, Danyang, Huang, Yawen, Frangi, Alejandro F., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
- Published
- 2017
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125. Multimodal Simulations in Live Cell Imaging
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Svoboda, David, Kozubek, Michal, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2017
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126. Deep MR to CT Synthesis Using Unpaired Data
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Wolterink, Jelmer M., Dinkla, Anna M., Savenije, Mark H. F., Seevinck, Peter R., van den Berg, Cornelis A. T., Išgum, Ivana, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
- Published
- 2017
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127. Adversarial Image Synthesis for Unpaired Multi-modal Cardiac Data
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Chartsias, Agisilaos, Joyce, Thomas, Dharmakumar, Rohan, Tsaftaris, Sotirios A., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
- Published
- 2017
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128. MRI-TRUS Image Synthesis with Application to Image-Guided Prostate Intervention
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Onofrey, John A., Oksuz, Ilkay, Sarkar, Saradwata, Venkataraman, Rajesh, Staib, Lawrence H., Papademetris, Xenophon, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
- Published
- 2016
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129. Pseudo-healthy Image Synthesis for White Matter Lesion Segmentation
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Bowles, Christopher, Qin, Chen, Ledig, Christian, Guerrero, Ricardo, Gunn, Roger, Hammers, Alexander, Sakka, Eleni, Dickie, David Alexander, Hernández, Maria Valdés, Royle, Natalie, Wardlaw, Joanna, Rhodius-Meester, Hanneke, Tijms, Betty, Lemstra, Afina W., van der Flier, Wiesje, Barkhof, Frederik, Scheltens, Philip, Rueckert, Daniel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2016
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130. Automatic Generation of Synthetic Retinal Fundus Images: Vascular Network
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Menti, Elisa, Bonaldi, Lorenza, Ballerini, Lucia, Ruggeri, Alfredo, Trucco, Emanuele, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
- Published
- 2016
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131. Geometry Regularized Joint Dictionary Learning for Cross-Modality Image Synthesis in Magnetic Resonance Imaging
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Huang, Yawen, Beltrachini, Leandro, Shao, Ling, Frangi, Alejandro F., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
- Published
- 2016
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132. Patch Based Synthesis of Whole Head MR Images: Application To EPI Distortion Correction
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Roy, Snehashis, Chou, Yi-Yu, Jog, Amod, Butman, John A., Pham, Dzung L., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
- Published
- 2016
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133. Whole Image Synthesis Using a Deep Encoder-Decoder Network
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Sevetlidis, Vasileios, Giuffrida, Mario Valerio, Tsaftaris, Sotirios A., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
- Published
- 2016
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134. PURE: Panoramic Ultrasound Reconstruction by Seamless Stitching of Volumes
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Flach, Barbara, Makhinya, Maxim, Goksel, Orcun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
- Published
- 2016
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135. GBM Modeling with Proliferation and Migration Phenotypes: A Proposal of Initialization for Real Cases
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Ortiz-Pla, Juan, Fuster-Garcia, Elies, Juan-Albarracin, Javier, Garcia-Gomez, Juan Miguel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
- Published
- 2016
- Full Text
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136. Generation of Realistic 4D Synthetic CSPAMM Tagged MR Sequences for Benchmarking Cardiac Motion Tracking Algorithms
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Zhou, Yitian, De Craene, Mathieu, Somphone, Oudom, Sermesant, Maxime, Bernard, Olivier, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
- Published
- 2016
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137. From Image-Based Modeling to the Modeling of Imaging with the Virtual Population
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Neufeld, Esra, Lloyd, Bryn, Kuster, Niels, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2016
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138. Registration of Pathological Images
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Yang, Xiao, Han, Xu, Park, Eunbyung, Aylward, Stephen, Kwitt, Roland, Niethammer, Marc, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2016
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139. Covering Population Variability: Morphing of Computation Anatomical Models
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Lloyd, Bryn, Cherubini, Emilio, Farcito, Silvia, Neufeld, Esra, Baumgartner, Christian, Kuster, Niels, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2016
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140. Image-Based PSF Estimation for Ultrasound Training Simulation
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Mattausch, Oliver, Goksel, Orcun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2016
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141. Microstructure Imaging Sequence Simulation Toolbox
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Ianuş, Andrada, Alexander, Daniel C., Drobnjak, Ivana, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2016
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142. Software Framework for Realistic MRI Simulations Using the Polyhedral Fourier Transform
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Han, Shuo, Herzka, Daniel A., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2016
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143. Numerical Simulation of Ultrasonic Backscattering During Fracture Healing Using Numerical Models Based on Scanning Acoustic Microscopy Images
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Potsika, Vassiliki T., Grivas, Konstantinos N., Gortsas, Theodoros, Protopappas, Vasilios C., Polyzos, Demosthenes, Fotiadis, Dimitrios I., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tsaftaris, Sotirios A., editor, Gooya, Ali, editor, Frangi, Alejandro F., editor, and Prince, Jerry L., editor
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- 2016
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144. DragNet: Learning-based deformable registration for realistic cardiac MR sequence generation from a single frame.
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Zakeri, Arezoo, Hokmabadi, Alireza, Bi, Ning, Wijesinghe, Isuru, Nix, Michael G., Petersen, Steffen E., Frangi, Alejandro F., Taylor, Zeike A., and Gooya, Ali
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- *
CARDIAC magnetic resonance imaging , *RECURRENT neural networks , *PROBABILISTIC generative models , *HEART beat , *HIGH resolution imaging , *MOVEMENT sequences , *VIDEO compression - Abstract
Deformable image registration (DIR) can be used to track cardiac motion. Conventional DIR algorithms aim to establish a dense and non-linear correspondence between independent pairs of images. They are, nevertheless, computationally intensive and do not consider temporal dependencies to regulate the estimated motion in a cardiac cycle. In this paper, leveraging deep learning methods, we formulate a novel hierarchical probabilistic model, termed DragNet, for fast and reliable spatio-temporal registration in cine cardiac magnetic resonance (CMR) images and for generating synthetic heart motion sequences. DragNet is a variational inference framework, which takes an image from the sequence in combination with the hidden states of a recurrent neural network (RNN) as inputs to an inference network per time step. As part of this framework, we condition the prior probability of the latent variables on the hidden states of the RNN utilised to capture temporal dependencies. We further condition the posterior of the motion field on a latent variable from hierarchy and features from the moving image. Subsequently, the RNN updates the hidden state variables based on the feature maps of the fixed image and the latent variables. Different from traditional methods, DragNet performs registration on unseen sequences in a forward pass, which significantly expedites the registration process. Besides, DragNet enables generating a large number of realistic synthetic image sequences given only one frame, where the corresponding deformations are also retrieved. The probabilistic framework allows for computing spatio-temporal uncertainties in the estimated motion fields. Our results show that DragNet performance is comparable with state-of-the-art methods in terms of registration accuracy, with the advantage of offering analytical pixel-wise motion uncertainty estimation across a cardiac cycle and being a motion generator. We will make our code publicly available. [Display omitted] • We propose an unsupervised probabilistic motion generative model (DragNet). • We infer probabilistic displacement fields in their original high dimensional spaces. • Generating high temporal resolution image sequences given only one reference frame. • Explicit modelling spatio-temporal uncertainties at the pixel level for motion fields. [ABSTRACT FROM AUTHOR]
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- 2023
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145. Statistical shape modeling of the left ventricle: myocardial infarct classification challenge
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Xingyu Zhang, Xènia Albà, Pau Medrano-Gracia, Allen Lu, Peter Claes, Daniel Rueckert, Pierre Ablin, Sotirios A. Tsaftaris, Xavier Pennec, J. E. Allen, Marc-Michel Rohé, Marco Pereanez, Kaleem Siddiqi, Jan Ehrhardt, Serkan Çimen, Nicolas Duchateau, Avan Suinesiaputra, Vicente Grau, Catarina Pinto, Alejandro F. Frangi, Ilkay Oksuz, Karim Lekadir, Anirban Mukhopadhyay, Mahdi Tabassian, Martino Alessandrini, Alistair A. Young, Luciano Teresi, Nripesh Parajuli, Matthias Wilms, Maxime Sermesant, Brett R. Cowan, Ali Gooya, Jan D'hooge, Wenjia Bai, Paolo Piras, Dennis Säring, Department of Anatomy and Medical Imaging [Auckland], Faculty of Medical and Health Sciences [Auckland], University of Auckland [Auckland]-University of Auckland [Auckland], Centre for Intelligent Machines (CIM), McGill University = Université McGill [Montréal, Canada], Departament de Tecnologies de la Informació i les Comunicacions [Barcelona] (ETIC / UPF), Universitat Pompeu Fabra [Barcelona] (UPF), Department of Cardiovascular Sciences [Leuven], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Department of electrical, electronic and information engineering 'GUGLIELMO MARCONI' [Bologna] (DEI), Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), Department of Engineering Science [Oxford], Institute of Biomedical Engineering [Oxford] (IBME), University of Oxford-University of Oxford, Department of Computing [London], Biomedical Image Analysis Group [London] (BioMedIA), Imperial College London-Imperial College London, Center for Computational Imaging and Simulation Technologies in Biomedicine [Sheffield] (CISTIB), University of Sheffield [Sheffield], Department of Electrical Engineering [KU Leuven] (KU-ESAT), Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institute of Medical Informatics [Lübeck], Universität zu Lübeck = University of Lübeck [Lübeck], Department of Electrical Engineering [Yale University], Yale University [New Haven], Department of Biomedical Engineering [Yale University], Zuse Institute Berlin (ZIB), IMT Institute for Advanced Studies [Lucca], E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Dipartimento di Ingegneria Strutturale e Geotecnica [Rome], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), University of Applied Sciences Wedel, Departimento di Matematica E-Fisica [Rome], Università degli Studi Roma Tre = Roma Tre University (ROMA TRE), Suinesiaputra, Avan, Ablin, Pierre, Alba, Xenia, Alessandrini, Martino, Allen, Jack, Bai, Wenjia, Cimen, Serkan, Claes, Peter, Cowan, Brett R, D'Hooge, Jan, Duchateau, Nicola, Ehrhardt, Jan, Frangi, Alejandro F, Gooya, Ali, Grau, Vicente, Lekadir, Karim, Lu, Allen, Mukhopadhyay, Anirban, Oksuz, Ilkay, Parajali, Nripesh, Pennec, Xavier, Pereanez, Marco, Pinto, Catarina, Piras, Paolo, Rohe, Marc-Michel, Rueckert, Daniel, Saring, Denni, Sermesant, Maxime, Siddiqi, Kaleem, Tabassian, Mahdi, Teresi, Luciano, Tsaftaris, Sotirios A, Wilms, Matthia, Young, Alistair A, Zhang, Xingyu, Medrano-Gracia, Pau, University of Oxford [Oxford]-University of Oxford [Oxford], Universität zu Lübeck [Lübeck], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], and Università degli Studi Roma Tre
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Computer science ,Feature extraction ,statistical shape analysis ,computer science applications1707 computer vision and pattern recognition ,030204 cardiovascular system & hematology ,computer.software_genre ,Article ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Health Information Management ,Medical imaging ,medicine ,[INFO.INFO-IM]Computer Science [cs]/Medical Imaging ,Myocardial infarction ,Electrical and Electronic Engineering ,myocardial infarct ,Ventricular remodeling ,Set (psychology) ,Computational model ,Receiver operating characteristic ,shape modelling ,business.industry ,Statistical shape analysis ,Pattern recognition ,Cardiac modeling ,classification ,biotechnology ,electrical and electronic engineering ,health information management ,medicine.disease ,3. Good health ,Computer Science Applications ,Artificial intelligence ,Data mining ,business ,computer ,Biotechnology - Abstract
Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes the left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to 1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and 2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website. 1 1 http://www.cardiacatlas.org .
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- 2018
146. SegMorph: Concurrent Motion Estimation and Segmentation for Cardiac MRI Sequences.
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Bi N, Zakeri A, Xia Y, Cheng N, Taylor ZA, Frangi AF, and Gooya A
- Abstract
We propose a novel recurrent variational network, SegMorph, to perform concurrent segmentation and motion estimation on cardiac cine magnetic resonance image (CMR) sequences. Our model establishes a recurrent latent space that captures spatiotemporal features from cine-MRI sequences for multitask inference and synthesis. The proposed model follows a recurrent variational auto-encoder framework and adopts a learnt prior from the temporal inputs. We utilise a multi-branch decoder to handle bi-ventricular segmentation and motion estimation simultaneously. In addition to the spatiotemporal features from the latent space, motion estimation enriches the supervision of sequential segmentation tasks by providing pseudo-ground truth. On the other hand, the segmentation branch helps with motion estimation by predicting deformation vector fields (DVFs) based on anatomical information. Experimental results demonstrate that the proposed method performs better than state-of-the-art approaches qualitatively and quantitatively for both segmentation and motion estimation tasks. We achieved an 81% average Dice Similarity Coefficient (DSC) and a less than 3.5 mm average Hausdorff distance on segmentation. Meanwhile, we achieved a motion estimation Dice Similarity Coefficient of over 79%, with approximately 0.14% of pixels displaying a negative Jacobian determinant in the estimated DVFs.
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- 2024
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147. 4D-Precise: Learning-based 3D motion estimation and high temporal resolution 4DCT reconstruction from treatment 2D+t X-ray projections.
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Zakeri A, Hokmabadi A, Nix MG, Gooya A, Wijesinghe I, and Taylor ZA
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- Humans, Imaging, Three-Dimensional methods, Algorithms, Image Processing, Computer-Assisted methods, Phantoms, Imaging, Movement, Motion, Deep Learning, Four-Dimensional Computed Tomography methods, Cone-Beam Computed Tomography methods, Respiration, Radiotherapy Planning, Computer-Assisted methods
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Background and Objective: In radiotherapy treatment planning, respiration-induced motion introduces uncertainty that, if not appropriately considered, could result in dose delivery problems. 4D cone-beam computed tomography (4D-CBCT) has been developed to provide imaging guidance by reconstructing a pseudo-motion sequence of CBCT volumes through binning projection data into breathing phases. However, it suffers from artefacts and erroneously characterizes the averaged breathing motion. Furthermore, conventional 4D-CBCT can only be generated post-hoc using the full sequence of kV projections after the treatment is complete, limiting its utility. Hence, our purpose is to develop a deep-learning motion model for estimating 3D+t CT images from treatment kV projection series., Methods: We propose an end-to-end learning-based 3D motion modelling and 4DCT reconstruction model named 4D-Precise, abbreviated from Probabilistic reconstruction of image sequences from CBCT kV projections. The model estimates voxel-wise motion fields and simultaneously reconstructs a 3DCT volume at any arbitrary time point of the input projections by transforming a reference CT volume. Developing a Torch-DRR module, it enables end-to-end training by computing Digitally Reconstructed Radiographs (DRRs) in PyTorch. During training, DRRs with matching projection angles to the input kVs are automatically extracted from reconstructed volumes and their structural dissimilarity to inputs is penalised. We introduced a novel loss function to regulate spatio-temporal motion field variations across the CT scan, leveraging planning 4DCT for prior motion distribution estimation., Results: The model is trained patient-specifically using three kV scan series, each including over 1200 angular/temporal projections, and tested on three other scan series. Imaging data from five patients are analysed here. Also, the model is validated on a simulated paired 4DCT-DRR dataset created using the Surrogate Parametrised Respiratory Motion Modelling (SuPReMo). The results demonstrate that the reconstructed volumes by 4D-Precise closely resemble the ground-truth volumes in terms of Dice, volume similarity, mean contour distance, and Hausdorff distance, whereas 4D-Precise achieves smoother deformations and fewer negative Jacobian determinants compared to SuPReMo., Conclusions: Unlike conventional 4DCT reconstruction techniques that ignore breath inter-cycle motion variations, the proposed model computes both intra-cycle and inter-cycle motions. It represents motion over an extended timeframe, covering several minutes of kV scan series., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
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- 2024
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148. Immune subtyping of melanoma whole slide images using multiple instance learning.
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Godson L, Alemi N, Nsengimana J, Cook GP, Clarke EL, Treanor D, Bishop DT, Newton-Bishop J, Gooya A, and Magee D
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- Humans, ROC Curve, Staining and Labeling, Workflow, Biomarkers, Melanoma diagnostic imaging, Melanoma genetics
- Abstract
Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential predictive biomarkers. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here, we attempt to overcome this by developing deep learning models to classify gigapixel haematoxylin and eosin (H&E) stained pathology slides, which are well established in clinical workflows, into these immune subgroups. We systematically assess six different multiple instance learning (MIL) frameworks, using five different image resolutions and three different feature extraction methods. We show that pathology-specific self-supervised models using 10x resolution patches generate superior representations for the classification of immune subtypes. In addition, in a primary melanoma dataset, we achieve a mean area under the receiver operating characteristic curve (AUC) of 0.80 for classifying histopathology images into 'high' or 'low immune' subgroups and a mean AUC of 0.82 in an independent TCGA melanoma dataset. Furthermore, we show that these models are able to stratify patients into 'high' and 'low immune' subgroups with significantly different melanoma specific survival outcomes (log rank test, P< 0.005). We anticipate that MIL methods will allow us to find new biomarkers of high importance, act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
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- 2024
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149. Toxicity Prediction in Pelvic Radiotherapy Using Multiple Instance Learning and Cascaded Attention Layers.
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Elhaminia B, Gilbert A, Lilley J, Abdar M, Frangi AF, Scarsbrook A, Appelt A, and Gooya A
- Abstract
Modern radiotherapy delivers treatment plans optimised on an individual patient level, using CT-based 3D models of patient anatomy. This optimisation is fundamentally based on simple assumptions about the relationship between radiation dose delivered to the cancer (increased dose will increase cancer control) and normal tissue (increased dose will increase rate of side effects). The details of these relationships are still not well understood, especially for radiation-induced toxicity. We propose a convolutional neural network based on multiple instance learning to analyse toxicity relationships for patients receiving pelvic radiotherapy. A dataset comprising of 315 patients were included in this study; with 3D dose distributions, pre-treatment CT scans with annotated abdominal structures, and patient-reported toxicity scores provided for each participant. In addition, we propose a novel mechanism for segregating the attentions over space and dose/imaging features independently for a better understanding of the anatomical distribution of toxicity. Quantitative and qualitative experiments were performed to evaluate the network performance. The proposed network could predict toxicity with 80% accuracy. Attention analysis over space demonstrated that there was a significant association between radiation dose to the anterior and right iliac of the abdomen and patient-reported toxicity. Experimental results showed that the proposed network had outstanding performance for toxicity prediction, localisation and explanation with the ability of generalisation for an unseen dataset.
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- 2023
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150. Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation.
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Attar R, Pereañez M, Gooya A, Albà X, Zhang L, de Vila MH, Lee AM, Aung N, Lukaschuk E, Sanghvi MM, Fung K, Paiva JM, Piechnik SK, Neubauer S, Petersen SE, and Frangi AF
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- Biological Specimen Banks, Female, Humans, Imaging, Three-Dimensional, Male, Pattern Recognition, Automated, United Kingdom, Heart Ventricles diagnostic imaging, Image Interpretation, Computer-Assisted methods, Magnetic Resonance Imaging, Cine methods, Models, Statistical, Neural Networks, Computer
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Population imaging studies generate data for developing and implementing personalised health strategies to prevent, or more effectively treat disease. Large prospective epidemiological studies acquire imaging for pre-symptomatic populations. These studies enable the early discovery of alterations due to impending disease, and enable early identification of individuals at risk. Such studies pose new challenges requiring automatic image analysis. To date, few large-scale population-level cardiac imaging studies have been conducted. One such study stands out for its sheer size, careful implementation, and availability of top quality expert annotation; the UK Biobank (UKB). The resulting massive imaging datasets (targeting ca. 100,000 subjects) has put published approaches for cardiac image quantification to the test. In this paper, we present and evaluate a cardiac magnetic resonance (CMR) image analysis pipeline that properly scales up and can provide a fully automatic analysis of the UKB CMR study. Without manual user interactions, our pipeline performs end-to-end image analytics from multi-view cine CMR images all the way to anatomical and functional bi-ventricular quantification. All this, while maintaining relevant quality controls of the CMR input images, and resulting image segmentations. To the best of our knowledge, this is the first published attempt to fully automate the extraction of global and regional reference ranges of all key functional cardiovascular indexes, from both left and right cardiac ventricles, for a population of 20,000 subjects imaged at 50 time frames per subject, for a total of one million CMR volumes. In addition, our pipeline provides 3D anatomical bi-ventricular models of the heart. These models enable the extraction of detailed information of the morphodynamics of the two ventricles for subsequent association to genetic, omics, lifestyle habits, exposure information, and other information provided in population imaging studies. We validated our proposed CMR analytics pipeline against manual expert readings on a reference cohort of 4620 subjects with contour delineations and corresponding clinical indexes. Our results show broad significant agreement between the manually obtained reference indexes, and those automatically computed via our framework. 80.67% of subjects were processed with mean contour distance of less than 1 pixel, and 17.50% with mean contour distance between 1 and 2 pixels. Finally, we compare our pipeline with a recently published approach reporting on UKB data, and based on deep learning. Our comparison shows similar performance in terms of segmentation accuracy with respect to human experts., (Crown Copyright © 2019. Published by Elsevier B.V. All rights reserved.)
- Published
- 2019
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